A Short-Term Traffic Flow Prediction Method Based on Kernel Extreme Learning Machine

Xing, Yiming; Ban, Xiaojuan; Liu, Ruoyi

doi:10.1109/bigcomp.2018.00089

Cited by 13 publications

(9 citation statements)

References 9 publications

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“…Extreme learning machine (ELM) is an efficient learning paradigm for a wide range of fields. Xing et al [34] used the kernel function method instead of the hidden layer method to overcome the variation problem and proposed a traffic flow prediction method to improve the accuracy of traffic flow prediction.…”

Section: Prediction Methods Of Traffic Informationmentioning

confidence: 99%

Dynamic Path Planning Algorithms With Load Balancing Based on Data Prediction for Smart Transportation Systems

et al. 2020

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In modern transportation, traffic congestion has become an urgent problem in large and medium-sized cities. In smart transportation systems, it is an effective solution to design load balancing path planning algorithms that can dynamically adapt to traffic conditions in order to avoid congestion. In this work, a traffic path planning algorithm based on data prediction (TPPDP) is proposed to find the path with the shortest travel time, which is built on a predictive model based on historical traffic data and current traffic information. Furthermore, a path planning algorithm based on data prediction with load balancing (TPPDP-LB) is also proposed, which combines the predicted information and the number of concurrent requests to achieve the path with shortest travel time while maintaining global load balancing. A specific distributed computing framework for TPPDP-LB algorithm is designed to reduce the runtime of the algorithm. The simulation results proved that both TPPDP and TPPDP-LB algorithms have the advantage of shortest travel time, and TPPDP-LB algorithm achieves load balancing of computing. It is also proved that the distributed computing framework designed for TPPDP-LP algorithm can effectively reduce the runtime of system as well as keep the accuracy of algorithm. INDEX TERMS Path planning algorithm, data prediction, load balancing, distributed computing, smart transportation.

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Section: Prediction Methods Of Traffic Informationmentioning

confidence: 99%

Dynamic Path Planning Algorithms With Load Balancing Based on Data Prediction for Smart Transportation Systems

et al. 2020

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“…In 2018, Wang and Chow [182] applied ELM with large-scale GPS data to help taxi drivers searching best routes. Similar to their research, more scientists have utilized ELM to predict traffic flow for drivers and governments [94,199]. And it has become a popular trend in transportation research field.…”

Section: Transportation Applicationmentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

279

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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“…Recently, a new RBF network called ELM has been verified in the benchmark regression and classification data sets very well [10,11]. For non-linear chaotic time series prediction problem, the ELM using sigmoid activation function can obtain high accuracy [12].…”

Section: Related Workmentioning

confidence: 99%

“…In formula ( 1), the initial ω and b value can be randomly assigned by the ELM theory, and the output weights β can be calculated by the least squares solution. If the training of ELM network aims to reach not only the smallest training error, but also the smallest norm of output weights, which means the ELM network should be trained to approximate arbitrary samples with zero error [11], there exist β , ω and b that make y i = t i hold true. Therefore, the compact vector version of ELM function could be expressed as follows:…”

Section: Elm Neural Networkmentioning

confidence: 99%

An effective co-evolutionary algorithm based on artificial bee colony and differential evolution for time series predicting optimization

Duan

2020

Complex Intell. Syst.

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Non-linear model optimization for predicting time series is a challenge problem. In Intelligent Transportation Systems (ITS) application, the indispensable short-term traffic flow prediction with big data makes the problem worst. To improve the prediction accuracy and ensure real-time performance in the big data environment, we propose a novel co-evolutionary artificial bee colony (ABC) improved by differential evolution (DE) optimization algorithm combined with a traffic flow predicting model trained by extreme learning machine (ELM) neural network. The proposed model can inherit the better generalization performance and the less training time consumption of the standard ELM, and can achieve a more balanced search strategy with the optimized weights and biases to overcome the random initialization deficiency of the typical ELM, and successfully obtain higher prediction accuracy compared with state-of-the-art methods. To verify the efficiency of the proposed model, we apply it to Lozi and Tent chaotic time series simulations and measured traffic flow time series experiments. Simulation and experimental results demonstrate that the proposed model has superior performance and competitive computational efficiency.

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A Short-Term Traffic Flow Prediction Method Based on Kernel Extreme Learning Machine

Cited by 13 publications

References 9 publications

Dynamic Path Planning Algorithms With Load Balancing Based on Data Prediction for Smart Transportation Systems

Dynamic Path Planning Algorithms With Load Balancing Based on Data Prediction for Smart Transportation Systems

A review on extreme learning machine

An effective co-evolutionary algorithm based on artificial bee colony and differential evolution for time series predicting optimization

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